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Statistical Rethinking: A Bayesian Course with Examples in R and
Stan builds readers' knowledge of and confidence in statistical
modeling. Reflecting the need for even minor programming in today's
model-based statistics, the book pushes readers to perform
step-by-step calculations that are usually automated. This unique
computational approach ensures that readers understand enough of
the details to make reasonable choices and interpretations in their
own modeling work. The text presents generalized linear multilevel
models from a Bayesian perspective, relying on a simple logical
interpretation of Bayesian probability and maximum entropy. It
covers from the basics of regression to multilevel models. The
author also discusses measurement error, missing data, and Gaussian
process models for spatial and network autocorrelation. By using
complete R code examples throughout, this book provides a practical
foundation for performing statistical inference. Designed for both
PhD students and seasoned professionals in the natural and social
sciences, it prepares them for more advanced or specialized
statistical modeling. Web Resource The book is accompanied by an R
package (rethinking) that is available on the author's website and
GitHub. The two core functions (map and map2stan) of this package
allow a variety of statistical models to be constructed from
standard model formulas.

Introduction to Functional Data Analysis provides a concise
textbook introduction to the field. It explains how to analyze
functional data, both at exploratory and inferential levels. It
also provides a systematic and accessible exposition of the
methodology and the required mathematical framework. The book can
be used as textbook for a semester-long course on FDA for advanced
undergraduate or MS statistics majors, as well as for MS and PhD
students in other disciplines, including applied mathematics,
environmental science, public health, medical research, geophysical
sciences and economics. It can also be used for self-study and as a
reference for researchers in those fields who wish to acquire solid
understanding of FDA methodology and practical guidance for its
implementation. Each chapter contains plentiful examples of
relevant R code and theoretical and data analytic problems. The
material of the book can be roughly divided into four parts of
approximately equal length: 1) basic concepts and techniques of
FDA, 2) functional regression models, 3) sparse and dependent
functional data, and 4) introduction to the Hilbert space framework
of FDA. The book assumes advanced undergraduate background in
calculus, linear algebra, distributional probability theory,
foundations of statistical inference, and some familiarity with R
programming. Other required statistics background is provided in
scalar settings before the related functional concepts are
developed. Most chapters end with references to more advanced
research for those who wish to gain a more in-depth understanding
of a specific topic.

Statistical Methods for SPC and TQM sets out to fill the gap for
those in statistical process control (SPC) and total quality
management (TQM) who need a practical guide to the logical basis of
data presentation, control charting, and capability indices.
Statistical theory is introduced in a practical context, usually by
way of numerical examples. Several methods familiar to
statisticians have been simplified to make them more accessible.
Suitable tabulations of these functions are included; in several
cases, effective and simple approximations are offered.
Contents
Data Collection and Graphical Summaries
Numerical Data Summaries-Location and Dispersion
Probability and Distribution
Sampling, Estimation, and Confidence
Sample Tests of Hypothesis; "Significance Tests"
Control Charts for Process Management and Improvement
Control Charts for Average and Variation
Control Charts for "Single-Valued" Observations
Control Charts for Attributes and Events
Control Charts: Problems and Special Cases
Cusum Methods
Process Capability-Attributes, Events, and Normally Distributed
Data
Capability; Non-Normal Distributions
Evaluating the Precision of a Measurement System (Gauge
Capability)
Getting More from Control Chart Data
SPC in "Non-Product" Applications
Appendices

This book provides an introduction to the use of statistical
concepts and methods to model and analyze financial data. The ten
chapters of the book fall naturally into three sections. Chapters 1
to 3 cover some basic concepts of finance, focusing on the
properties of returns on an asset. Chapters 4 through 6 cover
aspects of portfolio theory and the methods of estimation needed to
implement that theory. The remainder of the book, Chapters 7
through 10, discusses several models for financial data, along with
the implications of those models for portfolio theory and for
understanding the properties of return data. The audience for the
book is students majoring in Statistics and Economics as well as in
quantitative fields such as Mathematics and Engineering. Readers
are assumed to have some background in statistical methods along
with courses in multivariate calculus and linear algebra.

Statistics for Finance develops students' professional skills in
statistics with applications in finance. Developed from the
authors' courses at the Technical University of Denmark and Lund
University, the text bridges the gap between classical, rigorous
treatments of financial mathematics that rarely connect concepts to
data and books on econometrics and time series analysis that do not
cover specific problems related to option valuation. The book
discusses applications of financial derivatives pertaining to risk
assessment and elimination. The authors cover various statistical
and mathematical techniques, including linear and nonlinear time
series analysis, stochastic calculus models, stochastic
differential equations, Ito's formula, the Black-Scholes model, the
generalized method-of-moments, and the Kalman filter. They explain
how these tools are used to price financial derivatives, identify
interest rate models, value bonds, estimate parameters, and much
more. This textbook will help students understand and manage
empirical research in financial engineering. It includes examples
of how the statistical tools can be used to improve value-at-risk
calculations and other issues. In addition, end-of-chapter
exercises develop students' financial reasoning skills.

A fair question to ask of an advocate of subjective Bayesianism
(which the author is) is "how would you model uncertainty?" In this
book, the author writes about how he has done it using real
problems from the past, and offers additional comments about the
context in which he was working.

Provides an introduction to decision analysis. This book is based
upon a number of papers and articles taken from the Operational
Research Society's journal and other publications. However, the
book is not simply a 'collection of reprints': Professor French has
provided extensive notes and commentary to weave the extracts into
a coherent whole. Although techniques are presented, the main
thrust is to convey the purpose of decision analysis and the
interpretation that should be placed upon its output: vital topics,
but ones seldom discussed in introductory texts. The writing is
aimed at the non-technical reader.

A major tool for quality control and management, statistical
process control (SPC) monitors sequential processes, such as
production lines and Internet traffic, to ensure that they work
stably and satisfactorily. Along with covering traditional methods,
Introduction to Statistical Process Control describes many recent
SPC methods that improve upon the more established techniques. The
author-a leading researcher on SPC-shows how these methods can
handle new applications. After exploring the role of SPC and other
statistical methods in quality control and management, the book
covers basic statistical concepts and methods useful in SPC. It
then systematically describes traditional SPC charts, including the
Shewhart, CUSUM, and EWMA charts, as well as recent control charts
based on change-point detection and fundamental multivariate SPC
charts under the normality assumption. The text also introduces
novel univariate and multivariate control charts for cases when the
normality assumption is invalid and discusses control charts for
profile monitoring. All computations in the examples are solved
using R, with R functions and datasets available for download on
the author's website. Offering a systematic description of both
traditional and newer SPC methods, this book is ideal as a primary
textbook for a one-semester course in disciplines concerned with
process quality control, such as statistics, industrial and systems
engineering, and management sciences. It can also be used as a
supplemental textbook for courses on quality improvement and system
management. In addition, the book provides researchers with many
useful, recent research results on SPC and gives quality control
practitioners helpful guidelines on implementing up-to-date SPC
techniques.